JP7222176B2 - Electroencephalogram measurement method and electroencephalogram measurement device - Google Patents

Description

The present invention relates to an electroencephalogram measurement method and an electroencephalogram measurement apparatus.

As a technique for measuring electroencephalograms of a subject, for example, Patent Literature 1 describes an electroencephalogram measurement apparatus provided with an electroencephalogram cap having electrodes attached to the subject.

JP 2015-054240 A

In the conventional electroencephalogram measurement, the subject's body (for example, the head) is measured, and the ideal electrode positions are determined manually to arrange the electrodes.
However, since it is not always possible to place the electrodes at ideal positions every time, improper placement causes a decrease in measurement accuracy.
SUMMARY OF THE INVENTION It is an object of the present invention to suppress a decrease in measurement accuracy due to displacement of electrodes when measuring electroencephalograms.

An electroencephalogram measurement method according to one aspect of the present invention is an electroencephalogram measurement method for measuring an electroencephalogram of a subject using electrodes attached to the subject, wherein a characteristic signal generated in the subject and mixed in the electroencephalogram is a target to which the electrode should be placed. comparing a first propagation degree for propagating to the position and a second propagation degree for propagating the feature signal to the current arrangement position of the electrode, and based on the comparison result between the first propagation degree and the second propagation degree , to detect the deviation between the current arrangement position and the target position.

An electroencephalogram measurement method according to another aspect of the present invention is an electroencephalogram measurement method for measuring an electroencephalogram of a subject using electrodes attached to the subject, wherein a characteristic signal generated in the subject and mixed into the electroencephalogram is detected by placing the electrodes. Based on the first propagation degree that the characteristic signal propagates to the target position and the second propagation degree that the feature signal propagates to the current arrangement position of the electrode, the electroencephalogram signal detected by the electrode at the current arrangement position is converted to the target position Estimate the electroencephalogram signal detected at .

According to one aspect of the present invention, it is possible to suppress deterioration in measurement accuracy due to misalignment of electrodes when measuring electroencephalograms.

1 is a block diagram showing an example of an electroencephalogram measurement device according to an embodiment; FIG. It is a graph showing an example of a waveform of an electroencephalogram. FIG. 4 is a schematic diagram for explaining a method of measuring a readiness potential for exercise; FIG. 3 is a side view of the head showing the electrode placement method according to the International 10-20 method. FIG. 4 is a top view of the head showing the electrode placement method according to the International 10-20 method. 2 is a block diagram showing a first example of a functional configuration of a controller shown in FIG. 1; FIG. 4 is a graph showing an example of an amplitude value map; FIG. 4 is an explanatory diagram of a first example of a method for adjusting lateral positions of electrodes; FIG. 4 is an explanatory diagram of a first example of a method for adjusting the position of electrodes in the front-rear direction; 4 is a flowchart of an example of an electroencephalogram measurement method according to the first embodiment; FIG. 4 is an explanatory diagram of a first example of a calculation method of a conversion formula W for converting an electroencephalogram signal measured at a current electrode position into an electroencephalogram signal at a target position; FIG. 4 is an explanatory diagram of a method of converting an electroencephalogram signal measured at the current electrode position into an electroencephalogram signal at a target position; 9 is a flowchart of an example of an electroencephalogram measurement method according to the second embodiment; 4 is a graph showing an example of an attenuation coefficient map; FIG. 10 is an explanatory diagram of a second example of a method for adjusting lateral positions of electrodes; FIG. 10 is an explanatory diagram of a second example of a method for adjusting the position of the electrodes in the front-rear direction; 11 is a flowchart of an example of an electroencephalogram measurement method according to the third embodiment; FIG. 11 is an explanatory diagram of a second example of a calculation method of a conversion formula W for converting an electroencephalogram signal measured at a current electrode position into an electroencephalogram signal at a target position; FIG. 12 is a flowchart of an example of an electroencephalogram measurement method according to the fourth embodiment; FIG. FIG. 12 is a flowchart of an example of an electroencephalogram measurement method according to the fifth embodiment; FIG. FIG. 14 is a flowchart of an example of an electroencephalogram measurement method according to the sixth embodiment; FIG. FIG. 14 is a flowchart of an example of an electroencephalogram measurement method according to the seventh embodiment; FIG. 2 is a block diagram showing a second example of the functional configuration of the controller shown in FIG. 1; FIG. FIG. 21 is a flowchart of an example of an electroencephalogram measurement method according to an eighth embodiment; FIG. FIG. 21 is a flowchart of an example of an electroencephalogram measurement method according to the ninth embodiment; FIG.

Hereinafter, first to ninth embodiments of the present invention will be described with reference to the drawings. In the following description of the drawings, the same or similar parts are labeled with the same or similar reference numerals. Note that each drawing is schematic and may differ from the actual one. Further, the first to ninth embodiments of the present invention shown below are examples of apparatuses and methods for embodying the technical idea of the present invention, and the technical idea of the present invention is It does not specify the structure, arrangement, etc. of the following. Various modifications can be made to the technical idea of the present invention within the technical scope defined by the claims.

(First embodiment)
As an example of an electroencephalogram measuring apparatus according to an embodiment of the present invention, a case of a behavior prediction apparatus that is mounted in a vehicle and measures the motor readiness potential of the electroencephalogram of the driver to predict the behavior of the driver will be illustrated. However, the present invention is not limited to such a behavior prediction device, and can be widely applied to various electroencephalogram measurement methods and electroencephalogram measurement devices for measuring electroencephalograms of subjects.
The electroencephalogram measurement apparatus includes a controller 1, an electroencephalograph 2 connected to the controller 1, a driving state sensor group 3, a surrounding situation sensor group 4, an actuator group 5, and an output device 6, as shown in FIG. The controller 1, the electroencephalograph 2, the driving state sensor group 3, the surrounding situation sensor group 4, the actuator group 5, and the output device 6 can transmit and receive data, signals, and information to and from each other in a wired or wireless manner.

The controller 1 performs arithmetic and logic operations on the electroencephalogram data output from the electroencephalograph 2 for processing necessary for the operation performed by the electroencephalogram measurement apparatus according to the embodiment, and issues a control command ( A control circuit such as an electronic control unit (ECU) that outputs a control signal) to the actuator group 5 and the output device 6 . The controller 1 includes, for example, a processor 11, a storage device 12, and an input/output interface (not shown).

The processor 11 is a device such as a microprocessor equivalent to a central processing unit (CPU) including an arithmetic logic unit (ALU), a control circuit (control device), various registers and the like. The storage device 12 is composed of a semiconductor memory, disk media, or the like, and may include storage media such as a register, a cache memory, and a ROM and RAM used as a main storage device. For example, the processor 11 can execute a program (behavior prediction program) pre-stored in the storage device 12 and indicating a series of processes necessary for the operation of the electroencephalogram measuring apparatus according to the embodiment.

The controller 1 may have a programmable logic device (PLD) such as a field programmable gate array (FPGA), etc., and is equivalently set by software processing in a general-purpose semiconductor integrated circuit. A functional logic circuit or the like may be used. Each unit constituting the controller 1 may be configured from a single piece of hardware, or may be configured from separate hardware.

The electroencephalograph 2 detects electroencephalograms (brain activity) of a driver (human) who is a subject, and outputs detected electroencephalogram signals (electroencephalogram data) to the controller 1 . The electroencephalograph 2 includes an electrode group (plurality of electrodes) 21 for electroencephalogram measurement, an amplifier 22 for amplifying a plurality of electroencephalogram signals that are potential changes collected by the electrode group 21, and a plurality of electroencephalogram signals output from the amplifier 22, respectively. and an A/D converter 24 for sampling analog data of the extracted electroencephalogram signal at a predetermined sampling period and converting it into digital data. Part of the functions of the electroencephalograph 2 other than the electrode group 21 may be incorporated in the controller 1 .

The electroencephalograph 2 detects weak potential difference signals generated between a plurality of electrodes attached to the driver's head as electrical activity generated in the driver's brain. For example, the controller 1 calculates a motor readiness potential (MRP) generated in the driver's brain by frequency-analyzing the potential difference signal between the electroencephalogram data of each electrode detected by the electroencephalograph 2 .
The motor readiness potential is a type of event-related potential (ERP) indicating a brain reaction that appears as a result of thinking or cognition, and is a potential generated when a person tries to move a hand or leg voluntarily.

The electroencephalogram underlying the motor readiness potential occurs before the driver actually begins to act. Therefore, the ready-to-exercise potential is detected from about 2 seconds before the timing at which the driver starts to act, and is detected to be greater from about 400 ms before. Therefore, by calculating the exercise readiness potential, it is possible to predict the driver's action (behavioral intention) before the driver actually starts the action. Here, the case of calculating the exercise preparatory potential by frequency analysis is exemplified, but not limited to frequency analysis, pattern analysis may be used as long as signal analysis is possible.

FIG. 2 is a diagram showing an example of feature vectors in an electroencephalogram signal. Here, N feature amounts are extracted from the electroencephalogram signal before the driver starts to act, and an electroencephalogram feature vector P=(p1, p2, . . . , pN) is generated. For the feature amount, for example, values sampled at regular equal intervals are used. As shown in FIG. 3, a feature amount of past exercise readiness potentials is stored in a database in advance, and a region D to be arranged in the feature space is determined.

As for the definition of area D, for example, if there are a plurality of samples, the area D is determined as a circle whose center is the center of gravity of vector set {P} and whose radius is standard deviation σ. Then, it is determined whether or not the characteristic vector P of the exercise readiness potential measured from the driver in real time falls within the region D. Here, when the feature vector P falls within the region D, it is determined that the potential for preparation for exercise is generated, and when the feature vector P does not fall within the region D, it is determined that the potential for preparation for exercise is not generated.

Please refer to FIG. The controller 1 predicts the behavior of the driver by calculating the potential for readiness for exercise from the electroencephalogram signal measured by the electroencephalograph 2 . Further, when the driver's behavior is predicted, the controller 1 determines the driver's behavior based on the driving conditions of the vehicle detected by the driving condition sensor group 3 and the surrounding conditions of the vehicle detected by the surrounding condition sensor group 4. The intention is estimated, and a control command for assisting the driving of the vehicle is output to the actuator group 5 and the output device 6 .

The running state sensor group 3 includes a vehicle speed sensor 31 , an accelerator opening sensor 32 , a brake switch 33 , a steering operation amount sensor 34 and a blinker switch 35 . A vehicle speed sensor 31 detects the vehicle speed from the wheel speed of the vehicle and outputs the detected vehicle speed to the controller 1 . An accelerator opening sensor 32 detects the accelerator opening of the vehicle from the depression amount of the accelerator pedal, and outputs the detected accelerator opening to the controller 1 .

The brake switch 33 detects the operating state of the brake, such as whether or not the brake pedal of the vehicle is being operated, and outputs the detected operating state of the brake to the controller 1 . The steering operation amount sensor 34 detects the operation angle of the steering wheel of the vehicle or the steering angle of the steered wheels as the steering operation amount, and outputs the detected steering operation amount to the controller 1 . The winker switch 35 detects the operating state of the direction indicators (winkers), that is, the ON/OFF state of the left direction switch and the ON/OFF state of the right direction switch, and outputs the detected operating state of the winkers to the controller 1 .

Surrounding sensor group 4 includes camera 41 , radar 42 , map database (DB) 43 and global positioning system (GPS) receiver 44 . The camera 41 is an image sensor that acquires image data by imaging a predetermined range around the vehicle.
The camera 41 is, for example, a CCD wide-angle camera provided above the front window in the passenger compartment. The camera 41 may be a stereo camera or an omnidirectional camera, and may include multiple image sensors. From the acquired image data, the camera 41 detects roads and structures around the road, road markings, signs, other vehicles, pedestrians, etc. as surrounding conditions of the vehicle. Status data is output to the controller 1.

As the radar 42, a laser range finder (LRF) or a range finding radar using millimeter waves or ultrasonic waves can be adopted, for example, a millimeter wave radar provided in the front grille. The radar 42 scans a predetermined range around the vehicle to detect obstacles such as other vehicles existing around the vehicle. The radar 42 detects, for example, the relative position (azimuth) between an obstacle and the vehicle, the relative speed of the obstacle, the distance from the vehicle to the obstacle, and the like as the surrounding conditions of the vehicle. The radar 42 outputs data on the detected vehicle surroundings to the controller 1 .

A storage medium such as a semiconductor memory or a disk medium can be used as the map DB 43 . The map DB 43 stores map information including road types, road alignments, lane widths, vehicle traffic directions, speed limits, and the like. Alternatively, the map information database may be managed by a server, and only difference data of the updated map information may be acquired through, for example, a telematics service to update the map information stored in the map DB 43 . Note that the map DB 43 may be built in the storage device 12 of the controller 1 .

The GPS receiver 44 acquires the latitude, longitude and altitude of the vehicle as the current position of the vehicle based on radio waves received from satellites. The controller 1 collates the current position of the vehicle acquired by the GPS receiver 44 with the map stored in the map DB 43 to acquire the current position of the vehicle on the map stored in the map DB 43 . The controller 1 sets a travel route from the current position of the vehicle on the map to a destination set by the driver, etc. It is possible to control the running of the vehicle along the set running route.

For example, the controller 1 predicts the behavior of the driver based on the vehicle speed detected by the vehicle speed sensor 31 and the operating state of the blinkers detected by the blinker switch 35 when it is determined that the vehicle is stopped due to turning left or right. If so, it outputs a control command to the actuator group 5 so as to assist the start operation and the right/left turn operation.

The actuator group 5 includes an accelerator opening actuator 51, a brake control actuator 52, and a steering actuator 53, for example. The accelerator opening actuator 51 is composed of, for example, an electronically controlled throttle valve, and controls the accelerator opening of the vehicle based on a control command signal from the controller 1 . The brake control actuator 52 comprises a hydraulic circuit used for, for example, VDC (Vehicle Dynamics Control) or the like, and controls the braking operation of the brakes of the vehicle based on control command signals from the controller 1 . The steering actuator 53 controls steering of the vehicle based on a control command signal from the controller 1 .

Further, for example, when the behavior of the driver is predicted, the controller 1 outputs a control command to the output device 6 so as to output driving assistance information for assisting the driving of the vehicle. For example, when it is determined that the vehicle is stopped due to a left or right turn, if the driver's behavior is predicted, driving support information is output to notify the presence or absence of an object detected on the path after the turn. Output a control command to

The output device 6 has a display 61 and a buzzer 62 . The display 61 and the buzzer 62 present (notify) the intention of starting the vehicle to the surroundings of the vehicle, for example, based on the signal of the control command from the controller 1 .
Further, the display 61 and the buzzer 62 output driving support information based on control command signals from the controller 1 .

Next, mounting of the electrode group 21 for electroencephalogram measurement will be described. An electroencephalogram measurement electrode group 21 of the electroencephalograph 2 is arranged on the surface of the human head. For example, the electrode group 21 can be attached noninvasively by attaching the electrode group 21 to an electrode cap, a band, a net, or the like and wearing it on the head. The method of attaching the electrode group 21 is not particularly limited. For example, the electrodes may be fixed with a conductive paste or the like, or a needle-like electrode may be inserted into the scalp.

As a conventional electrode placement method for electroencephalogram measurement, an electrode placement method based on the International 10-20 method is generally used. The electrode placement method based on the International 10-20 method is, as shown in FIGS. 10%, 20%, 20%, 20%, 20%, 10%, and 21 electrodes Fp1, Fp2, F7, F3, Fz, F4, F8, A1, T3, C3, Cz , C4, T4, A2, T5, P3, Pz, P4, T6, O1, and O2.

Electrodes Fp1 and Fp2 are frontal poles (anatomically, anterior frontal lobe), electrodes F3 and F4 are frontal (anatomically, motor cortex), electrodes C3 and C4 are central regions (anatomically, central groove), electrodes P3 and P4 are the parietal region (anatomically the sensory area), electrodes O1 and O2 are the occipital region (anatomically the visual cortex), electrodes F7 and F8 are the anterior temporal region (anatomically lower frontal area), electrodes T3 and T4 for the middle temporal region (anatomically middle temporal lobe), electrodes T5 and T6 for the posterior temporal region (anatomically posterior temporal lobe), electrode A1, A2 is located at the ear lobe, electrode Fz is located at the midline frontal region, electrode Cz is located at the center midline, and electrode Pz is located at the midline parietal region.

4A and 4B with dashed lines, electrodes FC3, FCz, FC4 and electrodes CP3, CPz, CP4 are electrodes defined by the extended international 10-20 method (10% method), which is an extension of the international 10-20 method is. Electrodes FC3, FCz, FC4 are located in the frontal and central regions, respectively, between electrodes F3, Fz, F4 and electrodes C3, Cz, C4. Electrodes CP3, CPz, CP4 are located at the center and top of the head between electrodes C3, Cz, C4 and electrodes P3, Pz, P4, respectively.

A unipolar lead method, a bipolar lead method, and an average reference electrode method can be used as methods for measuring electroencephalograms. The unipolar induction method is a method in which an earlobe electrode or the like is used as a reference electrode and the potential difference between this reference electrode and an electrode on the surface of the head (probing electrode) is measured. The bipolar induction method is a method of measuring the potential difference between two points of each electrode on the head surface. The average reference electrode method is a method of measuring the potential difference between a point connecting all electrodes as a reference electrode and an electrode on the surface of the head (exploration electrode).

In order to accurately measure the subject's electroencephalogram, it is necessary to accurately mount the electrode group 21 at a predetermined arrangement position as shown in FIGS. 4A and 4B, for example. Conventionally, a person with knowledge of electroencephalogram measurement measures the size of the subject's head and determines the mounting position, thereby mounting the electrodes at the ideal placement position.
For this reason, if it is difficult to have a person with knowledge of EEG measurement determine the placement position every time, such as when repeatedly measuring EEG (for example, when measuring EEG on a daily basis), the ideal position There is a risk that the electrode group 21 will be arranged at a position that is out of alignment, resulting in a drop in measurement accuracy. Moreover, even a person who has knowledge of electroencephalogram measurement may misalign the placement position.

Therefore, in the embodiment of the present invention, the subject is caused to generate a predetermined feature signal, and the feature signal is mixed into the subject's electroencephalogram signal. The feature signal to be mixed in the electroencephalogram signal may be, for example, an artifact, such as a blink signal generated by a blinking action, or an ocular myoelectric potential generated by vertical or horizontal eye movement.
Then, the characteristic signal is detected by the electrode group 21, and the degree of propagation of the characteristic signal propagated to each electrode included in the electrode group 21 (hereinafter simply referred to as "each electrode") is measured.

Since the feature signal attenuates according to the propagation distance, the degree of propagation depends on the electrode position. Therefore, it is necessary to compare the degree of propagation of the characteristic signal detected at the ideal placement position of the electrodes with the degree of propagation of the characteristic signal detected at the placement position where the electrodes are attached when actually measuring the brain activity of the subject. Thus, it can be determined whether or not the position of the electrode at the time of actual measurement deviates from the ideal position.

Specifically, first, the electrode group 21 is arranged in advance at an ideal electrode arrangement position (that is, a target position where the electrode group 21 should be arranged). At this time, a person who has knowledge of electroencephalogram measurement may measure the size of the driver's head and determine the target position.
In this state, a characteristic signal is generated in the subject, and the first degree of propagation of the characteristic signal propagating to the target position is measured and recorded. Hereinafter, the target position where the electrode group 21 should be arranged is simply referred to as "target position".
For example, an electroencephalogram mixed with a characteristic signal may be measured by each electrode, and the degree of propagation of each electrode to a target position may be measured by detecting the characteristic signal contained in the measured electroencephalogram.

Next, when actually measuring brain activity of the subject, the electrode group 21 is worn again. At this time, it is not necessary to strictly measure the size of the driver's head to determine the placement position, as in the case of arranging the electrode group 21 at the target position. good too. In this way, the position where the electrode group 21 is arranged when actually measuring the brain activity of the subject is hereinafter referred to as the "current arrangement position".
A characteristic signal is generated in the subject in this state, and the degree of second propagation of the characteristic signal propagating to the current arrangement position of each electrode is measured.
Then, by comparing the first degree of propagation and the second degree of propagation, the deviation between the current arrangement position and the target position is detected. Therefore, by adjusting the arrangement position so that the detected deviation becomes small, the deviation from the ideal arrangement position can be reduced or eliminated.

The functional configuration of the controller 1 that detects the deviation between the current placement position and the target position will be described below.
Please refer to FIG. The controller 1 includes a propagation degree determination unit 70 , an electroencephalogram database (DB) 71 , a positional deviation detection unit 72 , a conversion equation determination unit 73 , an approximation unit 74 and a prediction unit 75 .
For example, the controller 1 causes the processor 11 to execute a computer program stored in the storage device 12, so that the propagation degree determination unit 70, the positional deviation detection unit 72, the conversion formula determination unit 73, the approximation unit 74, and the prediction unit 75 functions may be implemented.

The propagation degree determination unit 70 receives an electroencephalogram signal obtained by measurement with the electroencephalograph 2 . The propagation degree determination unit 70 detects the characteristic signal mixed in the electroencephalogram signal input from the electroencephalograph 2, and determines the amplitude value as the degree of propagation of the characteristic signal propagated to the arrangement position of each electrode.
The propagation degree determination unit 70 records the propagation degree determined based on the electroencephalogram signal detected by the electrode group 21 arranged at the target position in the electroencephalogram database 71 as the first propagation degree.
The propagation degree determination unit 70 may record an amplitude value map as shown in FIG. 6 in the electroencephalogram database 71 as the first propagation degree. The amplitude value map is obtained based on the distance from the nose root to each electrode Fz, Cz, Pz, Oz, . . . and the amplitude value of the feature signal detected at each electrode. shows the relationship between

Please refer to FIG. The propagation degree determination unit 70 outputs the propagation degree determined based on the electroencephalogram signal detected by the electrode group 21 arranged when the subject's brain activity is actually measured to the displacement detection unit 72 as the second propagation degree. Output.
The positional deviation detection unit 72 reads out the first degree of propagation from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the first degree of propagation and the second degree of propagation, and detects the deviation between the current arrangement position and the target position based on the comparison result.
For example, the positional deviation detection unit 72 may detect the positional deviation in the front-rear direction based on the comparison result between the first degree of propagation and the second degree of propagation.

For example, as shown in FIG. 7B, the positional deviation detection unit 72 may detect positional deviation in the front-rear direction based on a comparison between the amplitude value of the feature signal detected at the current arrangement position and an amplitude value map. In the case of the example of FIG. 7B, the amplitude value detected at the current arrangement position of the electrode Fz is smaller than the amplitude value at the target position, and the amplitude value on the amplitude value map at the position S1 shifted backward from the target position by the distance L equal to the value Therefore, it may be detected that the current arrangement position of the electrode Fz is shifted by the distance L behind the target position. On the other hand, since the amplitude value detected at the current arrangement position of the electrode Cz is larger than the amplitude value at the target position, it may be detected that the electrode Cz is shifted forward from the target position.

Further, for example, the positional deviation detection unit 72 compares the degree of the second propagation of two electrodes arranged bilaterally symmetrically (i.e., symmetrically with respect to the straight line L1 connecting the root of the nose PA and the occipital tubercle PB shown in FIG. 4B). However, lateral positional deviation may be detected based on the comparison result.
For example, in the case of blink signals, the degree of propagation to two symmetrically arranged electrodes (for example, electrodes C3 and C4) is equal to each other. Therefore, a difference in the second propagation degree at these electrode positions can be detected as a lateral displacement.

The positional deviation detection unit 72 outputs the detected deviation between the current arrangement position and the target position to the display 61 of the output device 6, for example.
For example, as shown in FIG. 7A, for two symmetrically arranged electrodes, the comparison result of the amplitude values detected at the current electrode positions may be displayed on the display 61 as the lateral displacement.
Further, for example, as shown in FIG. 7B, the comparison result between the amplitude value map and the amplitude value of the feature signal detected at the current arrangement position may be displayed on the display 61 as the positional deviation in the front-rear direction.

By referring to the comparison result displayed on the display 61, the placement position can be adjusted so as to reduce or eliminate the deviation between the current placement position and the target position.
When adjusting the arrangement position of each electrode, the horizontal position of each electrode is adjusted as a first step.
For example, in the first step, the lateral positions of the electrodes are adjusted so that the amplitude values detected by the two symmetrically arranged electrodes are equal. At this time, the positions of the electrodes in the front-rear direction are not changed.
In the example of FIG. 7A, since the amplitude value of the electrode C3 arranged on the left side is smaller than the amplitude value of the electrode C4 arranged on the right side, the electrodes C3 and C4 are displaced to the left from the target positions. I understand. Therefore, by moving electrodes C3 and C4 to the right, their amplitude values are made equal.

Next, as a second step, the position of each electrode in the front-rear direction is adjusted.
For example, in the second step, the longitudinal positions of the electrodes are adjusted so that the amplitude value measured at each electrode becomes equal to the amplitude value at the target position stored as the amplitude value map.
In the example of FIG. 7B, the amplitude value detected at the current placement position of the electrode Fz is smaller than the amplitude value at the target position, and is considered to be shifted by the distance L behind the target position. Therefore, by moving the position of the electrode Fz forward by the distance L, the amplitude value at the electrode Fz is made equal to the amplitude position at the target position.

Similarly, since the amplitude value detected at the current placement position of the electrode Cz is greater than the amplitude value at the target position, it is considered that the electrode Cz is shifted forward from the target position. Therefore, by moving the arrangement position of the electrode Cz backward, the amplitude value at the electrode Cz is made equal to the amplitude position at the target position.
After correcting the lateral position and the longitudinal position of each electrode in this way, the prediction unit 75 collects the electroencephalogram signal of the driver by the electroencephalograph 2 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the collected electroencephalogram signals.

When the driver's behavior is predicted, the prediction unit 75 predicts the driver's behavior based on the driving conditions of the vehicle detected by the driving condition sensor group 3 and the surrounding conditions of the vehicle detected by the surrounding condition sensor group 4. The intention is estimated, and a control command for assisting the driving of the vehicle is output to the actuator group 5 and the output device 6 .
The functions of the conversion formula determination unit 73 and the approximation unit 74 will be described later.

Next, an example of the electroencephalogram measurement method according to the first embodiment will be described with reference to the flowchart of FIG.
In step S1, the electrode group 21 is attached to a target position on the driver's head. In step S2, a characteristic signal is generated for the driver. In step S3, an electroencephalogram is measured by the electrode group 21 placed at the target position.

In step S4, the propagation degree determination unit 70 detects the amplitude value of the characteristic signal mixed in the electroencephalogram signal of the driver measured by the electrode group 21 in step S3. In step S5, the propagation degree determination unit 70 creates an amplitude value map representing the relationship between the distance from the nasal root and the amplitude value of the feature signal, and records it in the electroencephalogram database 71 as the first propagation degree.

In step S6, the electrode group 21 is attached to the driver's head when actually measuring the subject's brain activity. In step S7, a characteristic signal is generated for the driver. In step S8, electroencephalograms are measured with the electrode group 21 placed at the current arrangement position.
In step S9, the propagation degree determination unit 70 detects the amplitude value of the characteristic signal mixed in the electroencephalogram signal of the driver measured by the electrode group 21 in step S8 as the second degree of propagation of the characteristic signal propagated to the current arrangement position. do.

The positional deviation detection unit 72 reads the amplitude value map from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the amplitude value map and the second propagation degree, and detects positional deviation in the front-rear direction between the current arrangement position and the target position based on the comparison result. Further, the positional deviation detection unit 72 compares the second degree of propagation of the two symmetrically arranged electrodes, and detects lateral positional deviation based on the comparison result.

The positional deviation detection unit 72 outputs the detected deviation between the current arrangement position and the target position to the display 61 of the output device 6 .
In steps S10 and S11, the positional deviation of the electrode group 21 is corrected by referring to the deviation between the current arrangement position displayed on the display 61 and the target position. As a first step, the lateral position of the electrode group 21 is adjusted in step S10. As a second step, the position of the electrode group 21 in the front-rear direction is adjusted in step S11.
After correcting the placement position of the electrode group 21, the electroencephalograph 2 collects electroencephalogram signals of the driver. The prediction unit 75 predicts the behavior of the driver by calculating the potential for readiness for exercise from the electroencephalogram signal.

(Effect of the first embodiment)
(1) The electroencephalogram measurement method of the first embodiment measures electroencephalograms of a subject using the electrode group 21 attached to the subject. At this time, the subject is caused to generate a characteristic signal that is mixed with the electroencephalogram signal obtained by the electrode group 21 . Then, the first degree of propagation in which the characteristic signal propagates to the target position where the electrode should be arranged is compared with the second degree of propagation in which the characteristic signal propagates to the current arrangement position of the electrode. A deviation between the current arrangement position and the target position is detected based on the result of comparison between the first degree of propagation and the second degree of propagation.

This makes it possible to detect the deviation between the current arrangement position and the target position, and correct the current arrangement position based on the detected deviation, so that the electrode group 21 can be arranged at the ideal target position each time. . As a result, the accuracy of electroencephalogram measurement can be improved. Further, by measuring brain activity of the subject based on the electroencephalogram signals thus collected, the accuracy of brain activity measurement can be improved. For example, an event-related potential such as an exercise readiness potential can be calculated with high accuracy, and the prediction accuracy of the driver's behavior can be improved.

(2) The propagation degree determination unit 70 detects the amplitude value of the feature signal as the first degree of propagation and the second degree of propagation.
This makes it possible to detect the deviation between the current arrangement position and the target position using the amplitude value of the characteristic signal, and based on the detected deviation, the electrode group 21 can be arranged at the ideal target position each time. Therefore, the accuracy of electroencephalogram measurement can be improved.

(Second embodiment)
Next, a second embodiment will be described. The electroencephalogram measurement apparatus according to the second embodiment measures the current placement position of the electrode based on the first propagation degree at which the feature signal propagates to the target position and the second propagation degree at which the feature signal propagates to the current placement position of the electrode. By correcting (approximating) the electroencephalogram signal detected by the electrodes, the electroencephalogram signal detected at the target position is estimated.
For example, similarly to the first embodiment, the amplitude A1 of the feature signal propagated to the target position and the amplitude A2 of the feature signal propagated to the current arrangement position are detected as the first degree of propagation and the second degree of propagation, respectively.

Next, as shown in FIG. 9A, a conversion formula W for converting the electroencephalogram signal detected by the electrode at the current placement position to the electroencephalogram signal detected at the target position is calculated by W=A1/A2.
Then, as shown in FIG. 9B, when actually measuring the brain activity of the subject, the electroencephalogram signal detected at the target position is multiplied by the electroencephalogram signal detected by the electrode at the current placement position by the conversion formula W. to estimate

Please refer to FIG. The propagation degree determination unit 70 records the amplitude A1 of the characteristic signal detected when the electrode group 21 is placed at the target position in the electroencephalogram database 71 as the first propagation degree. Next, the propagation degree determination unit 70 converts the amplitude A2 of the characteristic signal detected when the electrode group 21 is placed at the current arrangement position when actually measuring brain activity of the subject, as a second propagation degree. It is output to the formula determination unit 73 .

The conversion formula determination unit 73 reads the amplitude A1 recorded as the first propagation degree in the electroencephalogram database 71, and calculates a conversion formula W (=A1/A2). Conversion formula determination unit 73 outputs conversion formula W to approximation unit 74 .
The approximation unit 74 reads, from the electroencephalograph 2, electroencephalogram signals detected by the electrodes arranged when the subject's brain activity is actually measured. The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 .

The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.
When the behavior of the driver is predicted, the prediction unit 75 estimates the intention of the driver's behavior based on the driving conditions of the vehicle and the surrounding conditions of the vehicle, and instructs the actuator group 5 and the output device 6 to drive the vehicle. Output control commands to assist.

Next, an example of an electroencephalogram measurement method according to the second embodiment will be described with reference to the flowchart of FIG.
The processing of steps S20 to S23 is the same as the processing of steps S1 to S4 in FIG. In step S24, the propagation degree determination unit 70 records the amplitude A1 of the characteristic signal detected when the electrode group 21 is placed at the target position in the electroencephalogram database 71 as the first propagation degree.

The processing of steps S25 to S28 is the same as the processing of steps S6 to S9 in FIG. In step S29, the propagation degree determination unit 70 outputs the amplitude A2 of the characteristic signal detected when the electrode group 21 is arranged at the current arrangement position to the conversion formula determination unit 73 as the second propagation degree. The conversion formula determination unit 73 reads the amplitude A1 recorded as the first propagation degree in the electroencephalogram database 71, and calculates a conversion formula W (=A1/A2).
In step S<b>30 , the approximation unit 74 reads from the electroencephalograph 2 electroencephalogram signals detected by the electrodes at the current placement positions. The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of Second Embodiment)
The electroencephalogram measurement method of the second embodiment measures electroencephalograms of a subject using the electrode group 21 attached to the subject. At this time, the subject is caused to generate a characteristic signal that is mixed with the electroencephalogram signal obtained by the electrode group 21 . Then, based on the first degree of propagation in which the characteristic signal propagates to the target position where the electrode should be arranged and the second degree of propagation in which the characteristic signal propagates to the current arrangement position of the electrode, the electrode at the current arrangement position is detected. An electroencephalogram signal detected at the target position is estimated from the obtained electroencephalogram signal.

As a result, the error in the electroencephalogram signal caused by the deviation between the current placement position and the target position can be corrected, and the waveform obtained when the electrodes are placed at the ideal position can be approximated, so the accuracy of electroencephalogram measurement can be improved. can be improved. Further, by measuring brain activity of the subject based on the electroencephalogram signals thus collected, the accuracy of brain activity measurement can be improved. For example, an event-related potential such as an exercise readiness potential can be calculated with high accuracy, and the prediction accuracy of the driver's behavior can be improved.

(Third embodiment)
Next, a third embodiment will be described. The electroencephalogram measurement apparatus according to the third embodiment calculates the attenuation coefficient of the characteristic signal propagated to the position of each electrode as the degree of propagation instead of the amplitude value of the first embodiment.
Here, the "attenuation coefficient" is calculated as a ratio of the detected value at the electrode position to the input value of the characteristic signal. The input value of the feature signal may be estimated from the electro-oculography detected by the electrode group arranged at the ideal target position, assuming that the electro-oculography mixed in the electroencephalogram is equal in magnitude each time.

Please refer to FIG. The propagation degree determination unit 70 detects each characteristic signal mixed in the electroencephalogram signal input from the electroencephalograph 2 when the electrode group 21 is placed at the target position, and divides these detected values by the input value of the characteristic signal. A first attenuation coefficient R1 is calculated as the first degree of propagation of the characteristic signal propagated to the arrangement position of each electrode.
The propagation degree determination unit 70 records in the electroencephalogram database 71 the first attenuation coefficient R1 calculated when the electrode group 21 is placed at the target position.
The propagation degree determination unit 70 may record an attenuation coefficient map as shown in FIG. 11 in the electroencephalogram database 71 as the first propagation degree. The attenuation coefficient map shows the relationship between the distance from the nasal root and the first attenuation coefficient R1.

Please refer to FIG. Next, the propagation degree determination unit 70 sends the second attenuation coefficient R2 calculated when the electrode group 21 is arranged when actually measuring brain activity of the subject to the positional deviation detection unit 72 as the second propagation degree. Output.
The positional deviation detection unit 72 reads out the first attenuation coefficient R1 from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the first damping coefficient R1 and the second damping coefficient R2, and detects the deviation between the current arrangement position and the target position based on the comparison result.
For example, the positional deviation detection section 72 may detect the positional deviation in the front-rear direction based on the result of comparison between the first damping coefficient R1 and the second damping coefficient R2.

For example, as shown in FIG. 12B, the positional deviation detection unit 72 may detect the positional deviation in the front-rear direction based on the comparison between the second damping coefficient R2 detected at the current arrangement position and the damping coefficient map. In the example of FIG. 12B, the second attenuation coefficient R2 detected at the current arrangement position of the electrode Fz is smaller than the first attenuation coefficient R1 at the target position, and the Equal to the amplitude value on the damping factor map. Therefore, it may be detected that the current arrangement position of the electrode Fz is shifted by the distance L behind the target position. On the other hand, since the second attenuation coefficient R2 detected at the current arrangement position of the electrode Cz is larger than the first attenuation coefficient R1 at the target position, it may be detected that the electrode Cz is deviated forward from the target position.

Further, for example, the positional deviation detection unit 72 may compare the second attenuation coefficients R2 of two electrodes arranged symmetrically, and detect lateral positional deviation based on the comparison result.
For example, in the case of a blink signal, the attenuation coefficients up to two symmetrically arranged electrodes (for example, electrodes C3 and C4) are equal to each other. Therefore, the difference in the second attenuation coefficient R2 at these electrode positions can be detected as a lateral displacement.
The positional deviation detection unit 72 outputs the detected deviation between the current arrangement position and the target position to the display 61 of the output device 6, for example.

For example, as shown in FIG. 7A, the display 61 displays the result of comparing the second attenuation coefficients R2 calculated for the current arrangement positions of the two electrodes arranged symmetrically, as a lateral displacement. good.
Further, as shown in FIG. 7B, the comparison result between the damping coefficient map and the second damping coefficient calculated for the current arrangement position may be displayed on the display 61 as the positional deviation in the front-rear direction.
By referring to the comparison result displayed on the display 61, the placement position can be adjusted so as to reduce or eliminate the deviation between the current placement position and the target position. The arrangement position adjustment method is the same as in the first embodiment.

After correcting the lateral position and the longitudinal position of each electrode, the prediction unit 75 collects the electroencephalogram signal of the driver by the electroencephalograph 2 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the collected electroencephalogram signals.
When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the third embodiment will be described with reference to the flowchart of FIG.
The processing of steps S40 to S42 is the same as the processing of steps S1 to S3 in FIG. In step S43, the propagation degree determination unit 70 calculates the first attenuation coefficient R1 of the characteristic signal detected when the electrode group 21 is placed at the target position. In step S44, the propagation degree determination unit 70 creates an attenuation coefficient map representing the relationship between the distance from the nasal root and the first attenuation coefficient R1, and records it in the electroencephalogram database 71 as the first propagation degree.

The processing of steps S45-S47 is the same as the processing of steps S6-S8 in FIG. In step S48, the propagation degree determination unit 70 calculates the second attenuation coefficient R2 of the characteristic signal detected when the electrode group 21 is arranged at the current arrangement position.
The positional deviation detection unit 72 reads out the attenuation coefficient map from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the damping coefficient map with the second damping coefficient R2, and detects the longitudinal positional deviation between the current arrangement position and the target position based on the comparison result. Further, the positional deviation detection unit 72 compares the second attenuation coefficients R2 of the two symmetrically arranged electrodes, and detects lateral positional deviation based on the comparison result.

The positional deviation detection unit 72 outputs the detected deviation between the current arrangement position and the target position to the display 61 of the output device 6 .
In steps S49 and S50, the positional deviation of the electrode group 21 is corrected by referring to the deviation between the current arrangement position displayed on the display 61 and the target position. As a first step, the lateral position of the electrode group 21 is adjusted in step S49. As a second step, the position of the electrode group 21 in the front-rear direction is adjusted in step S50.
After correcting the placement position of the electrode group 21, the electroencephalograph 2 collects electroencephalogram signals of the driver. The prediction unit 75 predicts the behavior of the driver by calculating the potential for readiness for exercise from the electroencephalogram signal.

(Effect of the third embodiment)
In the electroencephalogram measurement method of the third embodiment, the current arrangement position and the Detect deviation from the target position.
This makes it possible to detect the deviation between the current arrangement position and the target position using the attenuation coefficient of the characteristic signal, and based on the detected deviation, the electrode group 21 can be arranged at the ideal target position each time. Therefore, the accuracy of electroencephalogram measurement can be improved.

(Fourth embodiment)
Next, a fourth embodiment will be described. The electroencephalogram measurement apparatus according to the fourth embodiment corrects ( approximation) to estimate the electroencephalogram signal detected at the target position.
For example, as shown in FIG. 14, a conversion formula W (=R1/R2) is calculated for converting an electroencephalogram signal detected by the electrode at the current placement position into an electroencephalogram signal detected at the target position. When brain activity of the subject is actually measured, the electroencephalogram signal detected by the electrode at the current position is multiplied by the conversion formula W to estimate the electroencephalogram signal detected at the target position.

Please refer to FIG. The propagation degree determination unit 70 records the first attenuation coefficient R1 of the feature signal detected when the electrode group 21 is placed at the target position in the electroencephalogram database 71 as the first propagation degree. Next, the propagation degree determination unit 70 uses the second attenuation coefficient R2 of the characteristic signal detected when the electrode group 21 is arranged at the current arrangement position when actually measuring brain activity of the subject as the second propagation The degree is output to the conversion formula determination unit 73 .

The conversion formula determination unit 73 reads the first attenuation coefficient R1 recorded in the electroencephalogram database 71 and calculates a conversion formula W (=R1/R2). Conversion formula determination unit 73 outputs conversion formula W to approximation unit 74 .
The approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions when the subject's brain activity is actually measured. The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 .
The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.
When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the fourth embodiment will be described with reference to the flowchart of FIG.
The processing of steps S60-S63 is the same as the processing of steps S40-S43 in FIG. In step S64, the propagation degree determination unit 70 records the first attenuation coefficient R1 of the feature signal detected when the electrode group 21 is placed at the target position in the electroencephalogram database 71 as the first propagation degree.

The processing of steps S65-S68 is the same as the processing of steps S45-S48 in FIG. In step S69, the propagation degree determination unit 70 outputs the second attenuation coefficient R2 of the characteristic signal detected when the electrode group 21 is arranged at the current arrangement position to the conversion formula determination unit 73 as the second propagation degree. The conversion formula determination unit 73 reads the second attenuation coefficient R2 recorded in the electroencephalogram database 71 and calculates a conversion formula W (=R1/R2).
In step S70, the approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions. The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of the fourth embodiment)
The electroencephalogram measurement method of the fourth embodiment is based on the first attenuation coefficient R1 and the second attenuation coefficient R2 of the feature signal calculated as the first degree of propagation and the second degree of propagation, respectively, and detected by the electrode at the current arrangement position. An electroencephalogram signal detected at the target position is estimated from the obtained electroencephalogram signal.
As a result, the error in the electroencephalogram signal caused by the deviation between the current placement position and the target position is corrected using the attenuation coefficient of the feature signal, and the waveform obtained when the electrodes are placed at ideal positions can be approximated. can improve the accuracy of electroencephalogram measurement.

(Fifth embodiment)
Next, a fifth embodiment will be described. The electroencephalogram measurement apparatus according to the fifth embodiment calculates the first degree of propagation and the second degree of propagation by independent component analysis (ICA) that extracts a feature signal from the detection signal of each electrode of the electrode group 21. . Then, the deviation between the current arrangement position and the target position is detected based on the result of comparison between the first degree of propagation and the second degree of propagation.
For example, by predicting a square matrix W0 for obtaining independent components S = (s1, s2, ... sn) from electroencephalogram signals X = (x1, x2, ... xn) detected by each electrode, using independent component analysis, An electroencephalogram signal X is decomposed into independent components S. Here, electroencephalogram signals x1, x2, .

Next, any independent component sx corresponding to the feature signal is selected from the independent components S=(s1, s2, . . . sn).
Then, among the row vectors forming the square matrix W0, the row vectors (w1, w2 , . . . wn) are calculated.
Hereinafter, the row vector for calculating the feature signal sx from the electroencephalogram signal X will be referred to as "restoration operator", and its components w1, w2, . . . wn will be referred to as "weight" for each electrode.

The weights w1, w2, . Therefore, the weights w1, w2, . . . wn indicate the degree of propagation of the feature signal propagated to the arrangement position of each electrode.
Therefore, a restoration operator W1=(w11, w12, . The first propagation degree of the characteristic signal propagated to the target position of the electrode.

Similarly, a restoration operator W2=(w21, w22, . , the second propagation degree of the feature signal propagated to the current arrangement position of each electrode.
Then, based on the results of comparison between weights w11 and w21, weights w12 and w22, . to detect

Please refer to FIG. The propagation degree determination unit 70 decomposes the electroencephalogram signal X1 input from the electroencephalograph 2 when the electrode group 21 is placed at the target position into independent components S1 using independent component analysis. The propagation degree determination unit 70 selects the restoration operator W1 for extracting the feature signal sx from the electroencephalogram signal X1 from among the independent components S1 as the first propagation degree, and records it in the electroencephalogram database 71 .
Next, the propagation degree determination unit 70 converts the electroencephalogram signal X2 input from the electroencephalograph 2 when the electrode group 21 is arranged when actually measuring the subject's brain activity to the independent component S2 using independent component analysis. decompose into The propagation degree determination unit 70 selects the restoration operator W2 for extracting the feature signal sx from the electroencephalogram signal X2 as the second propagation degree, and outputs it to the displacement detection unit 72 as the second propagation degree.

The positional deviation detection unit 72 reads out the restoration operator W1 from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the restoration operator W1 and the restoration operator W2, and detects the deviation between the current arrangement position and the target position based on the comparison result.
For example, the positional deviation detection unit 72 uses conversion formulas φ1 (=w21/w11) and φ2 (= w22/w12), . . . φn (=w2n/w1n) are calculated. The conversion formulas φ1, φ2, . . . φn are collectively referred to as a conversion formula Φ.

The positional deviation detection section 72 may detect the positional deviation in the front-rear direction based on the conversion formula Φ. For example, when Φ>1, it is determined that the feature signal propagated to the current arrangement position is smaller than the feature signal propagated to the target position, and therefore the current arrangement position is shifted backward from the target position. you can Conversely, if Φ<1, it may be determined that the current arrangement position is shifted forward from the target position. When Φ=1, it may be determined that there is no positional deviation between the current arrangement position and the target position.

For example, the displacement detection unit 72 compares the weights of two symmetrically arranged electrodes among the weights w21, w22, . good too.
For example, in the case of blink signals, the degree of propagation to two symmetrically arranged electrodes (for example, electrodes C3 and C4) is equal to each other. Therefore, the weight ratio φt for these electrodes may be calculated as the lateral displacement.

For example, the weights for the electrodes C3 and C4 are set to w23 and w24, respectively, and their ratio φt=w23/w24 is calculated.
In the case of φt>1, the feature signal propagated to the electrode C3 arranged on the left side is smaller than the feature signal propagated to the electrode C4 arranged on the right side, so the electrodes C3 and C4 move left to the target position. It may be determined that the position is deviated. Conversely, if φt<1, it may be determined that the electrodes C3 and C4 are shifted to the right of the target position. When φt=1, it may be determined that the electrodes C3 and C4 are arranged symmetrically with respect to the straight line L1 connecting the nasal root PA and the occipital tubercle PB shown in FIG. 4B.

The positional deviation detection unit 72 outputs the detected deviation between the current arrangement position and the target position to the display 61 of the output device 6, for example.
By referring to the comparison result displayed on the display 61, the placement position can be adjusted so as to reduce or eliminate the deviation between the current placement position and the target position.
When adjusting the arrangement position of each electrode, the horizontal position of each electrode is adjusted as a first step.

For example, in the first step, the lateral positions of the electrodes are adjusted so that the weight ratio φt for two symmetrically arranged electrodes is 1 (that is, the weights are equal). At this time, the positions of the electrodes in the front-rear direction are not changed.
In the above example of electrodes C3 and C4, when φt>1, electrodes C3 and C4 are shifted to the left to the target position. In this case, φt is set to 1 by moving the electrodes C3 and C4 to the right. Conversely, if φt<1, φt is set to 1 by moving electrodes C3 and C4 to the left.

Next, as a second step, the position of each electrode in the front-rear direction is adjusted.
For example, in the second step, the position of each electrode in the front-rear direction is adjusted so that the conversion formula Φ becomes one. For example, if Φ>1, the current placement position is shifted backward from the target position. In this case, the conversion formula Φ is set to 1 by moving the electrodes forward. Conversely, if Φ<1, the transformation Φ is brought to 1 by moving the electrodes backwards.

After correcting the lateral position and the longitudinal position of each electrode in this way, the prediction unit 75 collects the electroencephalogram signal of the driver by the electroencephalograph 2 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the collected electroencephalogram signals.
When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of the electroencephalogram measurement method according to the fifth embodiment will be described with reference to the flowchart of FIG.
The processing of steps S80 to S82 is the same as the processing of steps S1 to S3 in FIG. In step S83, the propagation degree determination unit 70 decomposes the electroencephalogram signal X1 detected when the electrode group 21 is placed at the target position into independent components S1 using independent component analysis. In step S84, the propagation degree determination unit 70 selects the restoration operator W1 for extracting the feature signal sx from the electroencephalogram signal X1 among the independent components S1 as the first propagation degree. In step S<b>85 , the propagation degree determination unit 70 records the restoration operator W<b>1 in the electroencephalogram database 71 .

The processing of steps S86-S88 is the same as the processing of steps S6-S8 in FIG.
In step S89, the propagation degree determination unit 70 decomposes the electroencephalogram signal X2 detected when the electrode group 21 is placed at the current placement position into independent components S2 using independent component analysis. In step S90, the propagation degree determination unit 70 selects the restoration operator W2 for extracting the feature signal sx from the electroencephalogram signal X2 from among the independent components S2 as the second propagation degree.

In step S<b>91 , the positional deviation detection unit 72 reads the restoration operator W<b>1 from the electroencephalogram database 71 . The positional deviation detection unit 72 compares the restoration operator W1 and the restoration operator W2, and formulates a conversion formula for converting the electroencephalogram signal detected by each electrode at the current arrangement position into an electroencephalogram signal detected at each target position. Calculate Φ.
Further, the positional deviation detection unit 72 calculates a ratio φt of the weights of the restoration operator W2 to the two electrodes arranged symmetrically. The positional deviation detection unit 72 outputs the conversion formula Φ and the weight ratio φt to the display 61 of the output device 6 .

In step S92, the positional deviation of the electrode group 21 is corrected by referring to the deviation between the current arrangement position displayed on the display 61 and the target position. As a first step, the lateral position of the electrode group 21 is adjusted based on the weight ratio φt. As a second step, the position of the electrode group 21 in the front-rear direction is adjusted based on the conversion formula Φ.
After correcting the placement position of the electrode group 21, the electroencephalograph 2 collects electroencephalogram signals of the driver. The prediction unit 75 predicts the behavior of the driver by calculating the potential for readiness for exercise from the electroencephalogram signal.
When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

(Effect of the fifth embodiment)
In the electroencephalogram measurement method of the fifth embodiment, the difference between the current arrangement position and the target position is calculated based on the first propagation degree and the second propagation degree calculated by the independent component analysis that extracts the feature signal from the detection signal of the electrodes. to detect
This makes it possible to detect the deviation between the current arrangement position and the target position using independent component analysis, and based on the detected deviation, the electrode group 21 can be arranged at the ideal target position each time. It can improve the measurement accuracy of brain waves.

(Sixth embodiment)
Next, a sixth embodiment will be described. The electroencephalogram measurement apparatus according to the sixth embodiment determines the current arrangement position based on the first propagation degree and the second propagation degree calculated by independent component analysis that extracts the feature signal from the detection signal of each electrode of the electrode group 21. By correcting (approximating) the electroencephalogram signal detected by the electrodes, the electroencephalogram signal detected at the target position is estimated.

For example, based on the restoration operators W1 and W2 calculated as the first degree of propagation and the second degree of propagation, to convert the electroencephalogram signal detected by each electrode at the current placement position into the electroencephalogram signal detected at each target position. Calculate the conversion formula Φ.
When brain activity of the subject is actually measured, the electroencephalogram signal detected by the electrode at the current position is multiplied by the conversion formula W to estimate the electroencephalogram signal detected at the target position.

Please refer to FIG. The propagation degree determination unit 70 calculates a restoration operator W1 for extracting the feature signal sx from the electroencephalogram signal X1 detected when the electrode group 21 is placed at the target position.
The propagation degree determination unit 70 records the restoration operator W1 in the electroencephalogram database 71 as the first propagation degree.
Next, the propagation degree determination unit 70 uses a restoration operator W2 for extracting the characteristic signal sx from the electroencephalogram signal X2 detected when the electrode group 21 is placed at the current placement position when actually measuring brain activity of the subject. Calculate
Propagation degree determination section 70 outputs restoration operator W2 to conversion formula determination section 73 as the second propagation degree.
The conversion formula determining unit 73 reads out the restoration operator W1 recorded in the electroencephalogram database 71, and determines the conversion formulas Φ, that is, φ1 (=w21/w11), φ2 (=w22/w12), . . . φn (=w2n/w1n). calculate. Conversion formula determination section 73 outputs conversion formula Φ to approximation section 74 .

The approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions when the subject's brain activity is actually measured. The approximation unit 74 multiplies the read electroencephalogram signal by the conversion formula Φ to estimate the electroencephalogram signal detected at the target position. The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 .
The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.
When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the sixth embodiment will be described with reference to the flowchart of FIG.
The processing of steps S100 to S110 is the same as the processing of steps S80 to S90 in FIG. In step S<b>111 , the conversion formula determination unit 73 reads the restoration operator W<b>1 from the electroencephalogram database 71 . The conversion formula determination unit 73 compares the restoration operator W1 and the restoration operator W2, and determines a conversion formula for converting the electroencephalogram signal detected by each electrode at the current arrangement position into an electroencephalogram signal detected at each target position. Calculate Φ.
In step S<b>112 , the approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions. The approximation unit 74 multiplies the read electroencephalogram signal by the conversion formula Φ to estimate the electroencephalogram signal detected at the target position. The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of the sixth embodiment)
The electroencephalogram measurement method of the sixth embodiment is based on the first degree of propagation and the second degree of propagation calculated by independent component analysis that extracts the characteristic signal from the detection signal of each electrode of the electrode group 21, and the electrode at the current arrangement position. An electroencephalogram signal detected at the target position is estimated from the electroencephalogram signal detected in .
As a result, the error in the electroencephalogram signal caused by the deviation between the current placement position and the target position is corrected using the attenuation coefficient of the feature signal using independent component analysis, and when the electrode is placed at the ideal position Since the obtained waveform can be approximated, the accuracy of electroencephalogram measurement can be improved.

(Seventh embodiment)
Next, a seventh embodiment will be described. As described above, the weights w1, w2, . .
Therefore, the electroencephalogram measurement apparatus according to the seventh embodiment uses the first attenuation coefficient R1 of the feature signal propagated to the target position and the current current of the electrode based on the weights of the restoration operators W1 and W2 obtained using the independent component analysis. A second attenuation coefficient R2 of the feature signal propagated to the arrangement position is obtained, and these are defined as the first degree of propagation and the second degree of propagation, respectively. For example, the reciprocal of the weight may be calculated as the damping factor.
Then, the electroencephalogram signal detected at the target position is estimated by correcting (approximating) the electroencephalogram signal detected by the electrodes at the current placement position based on the first attenuation coefficient R1 and the second attenuation coefficient R2.

Please refer to FIG. The propagation degree determination unit 70 calculates the first attenuation coefficients r11=1/w11, r12=1/w12, . . The first damping coefficients r11, r12, . . . r1n are collectively referred to as "first damping coefficient R1". The propagation degree determination unit 70 records the first attenuation coefficient R1 in the electroencephalogram database 71 .
Next, based on the restoration operator W2, the propagation degree determination unit 70 uses the second attenuation coefficient r21= 1/w21, r22=1/w22, . . . r2n=1/w2n are calculated. The second damping coefficients r21, r22, . . . r2n are collectively referred to as "second damping coefficient R2".

The conversion formula determination unit 73 reads the first attenuation coefficient R1 recorded in the electroencephalogram database 71 . The conversion formula determination unit 73 determines a conversion formula W (=R1/R2) for converting the electroencephalogram signal detected by each electrode at the current arrangement position into the electroencephalogram signal detected at each target position, that is, r11/r21, Calculate r12/r22, . . . r1n/r2n. Conversion formula determination unit 73 outputs conversion formula W to approximation unit 74 .
The approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions when the subject's brain activity is actually measured.

The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal. When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the seventh embodiment will be described with reference to the flowchart of FIG.
The processing of steps S120 to S123 is the same as the processing of steps S80 to S83 in FIG. In step S124, the propagation degree determination unit 70 selects the restoration operator W1 for extracting the feature signal sx from the electroencephalogram signal X1 among the independent components S1 as the first propagation degree. The propagation degree determination unit 70 obtains the first attenuation coefficient R1 of the characteristic signal propagated to the target position of each electrode based on the restoration operator W1. In step S<b>125 , the propagation degree determination unit 70 records the first attenuation coefficient R<b>1 in the electroencephalogram database 71 .

The processing of steps S126-S129 is the same as the processing of steps S86-S89 in FIG. In step S130, the propagation degree determination unit 70 selects the restoration operator W2 for extracting the feature signal sx from the electroencephalogram signal X2 from among the independent components S2 as the second propagation degree. The propagation degree determination unit 70 obtains the second attenuation coefficient R2 of the feature signal propagated to the current arrangement position based on the restoration operator W2.
In step S<b>131 , the conversion formula determination unit 73 reads the first attenuation coefficient R<b>1 recorded in the electroencephalogram database 71 . The conversion formula determination unit 73 calculates a conversion formula W (=R1/R2) for converting the electroencephalogram signal detected by each electrode at the current arrangement position into the electroencephalogram signal detected at each target position.
In step S<b>132 , the approximation unit 74 reads from the electroencephalograph 2 the electroencephalogram signals detected by the electrodes at the current placement positions. The approximation unit 74 estimates the electroencephalogram signal detected at the target position by multiplying the read electroencephalogram signal by the conversion formula W. FIG. The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of the seventh embodiment)
In the electroencephalogram measurement method of the seventh embodiment, a first attenuation coefficient R1 and a second attenuation coefficient R2 are calculated based on the restoration operators W1 and W2 obtained by independent component analysis, and these first attenuation coefficient R1 and second attenuation coefficient Based on R2, the electroencephalogram signal detected at the target position is estimated from the electroencephalogram signal detected by the electrode at the current placement position.
As a result, the error in the electroencephalogram signal caused by the deviation between the current placement position and the target position is corrected using the attenuation coefficient of the feature signal, and the waveform obtained when the electrodes are placed at the ideal position is approximated. Therefore, the accuracy of electroencephalogram measurement can be improved.

(Eighth embodiment)
Next, an eighth embodiment will be described. The electroencephalogram measurement apparatus according to the eighth embodiment calculates, through machine learning, a learning model W for converting electroencephalogram signals from electrodes placed during actual brain activity measurement into electroencephalogram signals detected at each target position. do.
For example, the feature signal detected by the electrode placed at the target position is used as the target variable, and the feature signal detected by the electrode placed at the current position is used as the explanatory variable when actually measuring the brain activity of the subject. Calculate the model W.
Thereafter, based on the learning model W, the electroencephalogram signal detected at the target position is estimated by correcting (approximating) the electroencephalogram signal detected by the electrode at the current placement position.

See FIG. The controller 1 includes a conversion formula determination section 73 , an approximation section 74 , a prediction section 75 and an electroencephalogram data recording section 76 . For example, the controller 1 executes the computer program stored in the storage device 12 with the processor 11, thereby realizing the functions of the conversion formula determination unit 73, the approximation unit 74, the prediction unit 75, and the electroencephalogram data recording unit 76. you can

The electroencephalogram data recording unit 76 inputs an electroencephalogram signal mixed with a characteristic signal from the electroencephalograph 2 in which the electrode group 21 is arranged at the target position, and records it in the electroencephalogram database 71 . The conversion formula determination unit 73 reads the electroencephalogram signal detected by the electrode group 21 arranged at the target position from the electroencephalogram database 71 and inputs it as an objective variable.
The conversion formula determination unit 73 receives, as an explanatory variable, an electroencephalogram signal mixed with a feature signal from the electroencephalograph 2 in which the electrode group 21 is arranged when measuring the actual brain activity.

The conversion formula determination unit 73 corrects (approximates) the electroencephalogram signal detected by the electrode at the current placement position based on these objective variables and explanatory variables, thereby estimating the electroencephalogram signal detected at the target position. A learning model W is calculated by machine learning.
The learning model W is obtained, for example, from electroencephalogram signals X=(x1, x2, . =(y1,y2,...yn) may be an approximate polynomial function for estimating. The conversion formula determination unit 73 may learn parameters such as each coefficient of the polynomial function by machine learning. The learning method may be, for example, multiple regression analysis, deep learning, neural network, or the like.
In this way, the conversion formula determination unit 73 detects the positional deviation between the current electrode placement position and the target position by calculating the coefficient of the polynomial function.

The approximation unit 74 receives an electroencephalogram signal from the electroencephalograph 2 in which the electrode group 21 is arranged when measuring actual brain activity. The approximation unit 74 estimates an electroencephalogram signal detected at the target position from the input electroencephalogram signal based on the learning model W calculated by the conversion formula determination unit 73 . The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 .
The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal. When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the eighth embodiment will be described with reference to the flowchart of FIG.
The processing of steps S140-S142 is the same as the processing of steps S1-S3 in FIG. In step S<b>143 , the electroencephalogram data recording unit 76 inputs the electroencephalogram signal mixed with the characteristic signal from the electroencephalograph 2 in which the electrode group 21 is arranged at the target position, and records it in the electroencephalogram database 71 .

The processing of steps S144-S146 is the same as the processing of steps S6-S8 in FIG. In step S147, the conversion formula determination unit 73 inputs the electroencephalogram signal recorded in step S143 as an objective variable. The conversion formula determination unit 73 receives electroencephalogram signals mixed with characteristic signals from the electroencephalograph 2 in which the electrode group 21 is arranged for actual measurement of brain activity, and uses them as explanatory variables. Based on these objective variables and explanatory variables, the conversion formula determining unit 73 uses machine learning to create a learning model W for estimating the electroencephalogram signal detected at the target position from the electroencephalogram signal detected by the electrode at the current placement position. calculate.

In step S148, the approximation unit 74 inputs an electroencephalogram signal from the electroencephalograph 2 in which the electrode group 21 is arranged for actual measurement of brain activity. The approximation unit 74 estimates an electroencephalogram signal detected at the target position from the input electroencephalogram signal based on the learning model W calculated by the conversion formula determination unit 73 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of the eighth embodiment)
(1) The electroencephalogram measurement method of the eighth embodiment uses the feature signal detected by the electrode placed at the target position as the target variable and the feature signal detected by the electrode at the current placement position as the explanatory variable. , to detect the deviation between the current arrangement position and the target position.
As a result, it is possible to detect the deviation between the current arrangement position and the target position using machine learning, and based on the detected deviation, the electrode group 21 can be arranged at the ideal target position each time. can improve the measurement accuracy of

(2) The electroencephalogram measurement method of the eighth embodiment is based on a learning model in which the feature signal detected by the electrode placed at the target position is used as the objective variable and the feature signal detected by the electrode at the current placement position is used as the explanatory variable. Based on this, the electroencephalogram signal detected at the target position is estimated from the electroencephalogram signal detected by the electrode at the current placement position.
As a result, an error in the electroencephalogram signal caused by the deviation between the current placement position and the target position is corrected using the attenuation coefficient of the feature signal using machine learning, and the electrode is obtained when the electrode is placed at the ideal position. Since the waveform obtained can be approximated, the accuracy of electroencephalogram measurement can be improved.

(Ninth embodiment)
Next, a ninth embodiment will be described. An electroencephalogram measurement apparatus according to the ninth embodiment includes a first learning model W1 that extracts a feature signal from a detection signal of an electrode placed at a target position, and an electrode that is placed when actually measuring brain activity of a subject. A second learning model W2 for extracting a feature signal from the detection signal of is calculated by machine learning.
The first learning model W1 and the second learning model W2 may be conversion formulas similar to the above-described square matrix W0 for separating independent components from electroencephalogram signals in independent component analysis, for example.
Then, based on the first learning model W1 and the second learning model W2, the electroencephalogram signals of the electrodes arranged when actually measuring the subject's brain activity are corrected (approximated), and detected at the target position. A conversion formula Φ for estimating an electroencephalogram signal is calculated. The conversion formula Φ may be calculated, for example, in the same manner as in the sixth embodiment or the seventh embodiment.

See FIG. The conversion formula determination unit 73 inputs an electroencephalogram signal mixed with a feature signal from the electroencephalograph 2 in which the electrode group 21 is arranged at the target position, and selects a first learning model W1 that extracts a feature component from the input electroencephalogram signal. Calculated by machine learning. The conversion formula determination unit 73 records the calculated first learning model W1 in the electroencephalogram database 71 .
Next, a second learning model W2 is provided for actually measuring brain activity by inputting an electroencephalogram signal mixed with a feature signal from the electroencephalograph 2 in which the electrode group 21 is arranged and extracting a feature component from the input electroencephalogram signal. Calculated by machine learning.

The conversion formula determination unit 73 compares the first learning model W1 and the second learning model W2 in the same manner as in the sixth and seventh embodiments, so that when actually measuring brain activity of the subject, A conversion formula Φ for estimating an electroencephalogram signal detected at the target position is calculated by correcting (approximating) the electroencephalogram signal of the electrodes arranged in the position.
The approximation unit 74 receives an electroencephalogram signal from the electroencephalograph 2 in which the electrode group 21 is arranged when measuring actual brain activity. The approximation unit 74 estimates an electroencephalogram signal detected at the target position from the input electroencephalogram signal based on the conversion formula Φ calculated by the conversion formula determination unit 73 . The approximation unit 74 outputs the estimated electroencephalogram signal to the prediction unit 75 .
The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal. When the driver's action is predicted, the prediction unit 75 estimates the driver's action intention and outputs a control command to the actuator group 5 and the output device 6 to assist the driving of the vehicle.

Next, an example of an electroencephalogram measurement method according to the ninth embodiment will be described with reference to the flowchart of FIG.
The processing of steps S150-S152 is the same as the processing of steps S1-S3 in FIG. In step S153, the conversion formula determining unit 73 calculates a first learning model W1 for extracting characteristic components from the electroencephalogram signal input from the electroencephalograph 2 with the electrode group 21 arranged at the target position. In step S<b>154 , the conversion formula determination unit 73 records the first learning model W<b>1 in the electroencephalogram database 71 .

The processing of steps S155-S157 is the same as the processing of steps S6-S8 in FIG. In step S158, the conversion formula determination unit 73 calculates a second learning model W2 for extracting characteristic components from the electroencephalogram signal input from the electroencephalograph 2 in which the electrode group 21 is arranged for actual brain activity measurement. In step S159, the conversion formula determination unit 73 compares the first learning model W1 and the second learning model W2 to determine the target position from the electroencephalogram signals of the electrodes arranged when the subject's brain activity is actually measured. A conversion formula Φ for estimating the electroencephalogram signal detected in is calculated.
The approximation unit 74 receives an electroencephalogram signal from the electroencephalograph 2 in which the electrode group 21 is arranged when measuring actual brain activity. The approximation unit 74 estimates an electroencephalogram signal detected at the target position from the input electroencephalogram signal based on the conversion formula Φ calculated by the conversion formula determination unit 73 . The prediction unit 75 predicts the behavior of the driver by calculating the potential for preparation for exercise from the estimated electroencephalogram signal.

(Effect of the ninth embodiment)
The electroencephalogram measurement method of the ninth embodiment includes a first learning model W1 that extracts a feature signal from a detection signal of an electrode placed at a target position, and an electrode that is actually placed when measuring brain activity of a subject. A second learning model W2 for extracting a feature signal from the detection signal is calculated by machine learning. Then, based on the result of comparison between the first learning model W1 and the second learning model W2, the electroencephalogram signal detected at the target position from the electroencephalogram signal of the electrodes arranged when actually measuring the brain activity of the subject is obtained. presume.
As a result, errors in the electroencephalogram signal caused by the deviation between the current placement position and the target position are corrected using the attenuation coefficient of the feature signal obtained by machine learning, and the electrode is placed at the ideal position. Since the waveform obtained can be approximated, the accuracy of electroencephalogram measurement can be improved.

Of course, the invention includes various embodiments and the like that are not described here. Therefore, the technical scope of the present invention is defined only by the matters specifying the invention according to the valid scope of claims based on the above description.

DESCRIPTION OF SYMBOLS 1... Controller, 2... Electroencephalograph, 3... Running state sensor group, 4... Surrounding condition sensor group, 5... Actuator group, 6... Output device, 11... Processor, 12... Storage device, 21... Electrode group, 22... Amplifier , 23... filter, 24... converter, 31... vehicle speed sensor, 32... accelerator opening sensor, 33... brake switch, 34... steering operation amount sensor, 35... blinker switch, 41... camera, 42... radar, 43... map Database 44 Global Positioning System (GPS) receiver 51 Accelerator opening actuator 52 Brake control actuator 53 Steering actuator 61 Display 62 Buzzer 70 Propagation degree determination unit 71 Electroencephalogram Database 72 Detector 73 Transformation formula determiner 74 Approximator 75 Predictor 76 Electroencephalogram data recorder

Claims (7)

An electroencephalogram measurement method for measuring an electroencephalogram of a subject using electrodes attached to the subject,
A first propagation degree at which the feature signal generated in the subject and mixed in the electroencephalogram propagates to a target position where the electrode should be placed, and a first propagation degree at which the feature signal propagates to the current placement position of the electrode. estimating an electroencephalogram signal detected at the target position from the electroencephalogram signal detected at the electrode at the current position based on a second degree of propagation. 2. The electroencephalogram measuring method according to claim 1 , wherein attenuation coefficients of said characteristic signals detected by said electrodes are calculated as said first degree of propagation and said second degree of propagation. 2. The electroencephalogram measuring method according to claim 1 , wherein the first degree of propagation and the second degree of propagation are calculated by independent component analysis for extracting the feature signal from the detection signal of the electrodes. 2. The electroencephalogram measuring method according to claim 1 , wherein amplitude values of said characteristic signals detected by said electrodes are detected as said first degree of propagation and said second degree of propagation. The current placement based on a learning model trained using the feature signal detected by the electrode placed at the target position as an objective variable and the feature signal detected by the electrode at the current placement position as an explanatory variable. 2. The electroencephalogram measurement method according to claim 1 , wherein the electroencephalogram signal detected at the target position is estimated from the electroencephalogram signal detected by the electrode at the position. A first learning model that extracts the feature signal from the detection signal of the electrode placed at the target position, and a second learning model that extracts the feature signal from the detection signal of the electrode at the current placement position. Calculated by machine learning,
estimating an electroencephalogram signal detected at the target position from an electroencephalogram signal detected by the electrode at the current arrangement position based on a comparison result between the first learning model and the second learning model; The electroencephalogram measurement method according to claim 1 . an electroencephalograph that measures electroencephalograms of the subject using electrodes attached to the subject;
A first propagation degree at which the feature signal generated in the subject and mixed in the electroencephalogram propagates to a target position where the electrode should be placed, and a first propagation degree at which the feature signal propagates to the current placement position of the electrode. a controller for estimating an electroencephalogram signal detected at the target position from the electroencephalogram signal detected at the electrode at the current placement position, based on a second degree of propagation;
An electroencephalogram measurement device comprising:

Images